1. Technical Field
This disclosure generally relates to identification of materials based on hyperspectral imagery.
2. Background
Over the past decade, there has been an increasing need to characterize and view the composition and location of materials on the Earth's surface from a geospatial perspective. This need spans a diverse set of domains, with applications relevant to mineralogy and geology, forestry and agriculture, environmental monitoring, astronomy, as well as defense and security. One technology, hyperspectral imaging, has matured significantly over the past decade and emerged with the capability to address this gap through passive remote sensing.
Hyperspectral imagers measure electromagnetic energy as a function of wavelength for a given spatial sampling distance and total area. The result can be represented as a three-dimensional cube, where each spectral slice through the cube consists of a spatial image of measured energy for a single wavelength hand. In contrast to multispectral imagers, hyperspectral imagers typically sample narrow hands of energy across a nearly continuous spectral range. This sampling results in a vector, or spectral signature, of many tens or hundreds of energy measurements for each pixel in the image. Each signature represents a physical quantity linked by the underlying physics and chemistry to a particular material or mixture of materials within the sampled pixel. By comparing the signature of an image pixel to a library of reference signatures, one may be able to assign the pixel a material label. This is the foundation of the non-literal, i.e., spectral and not spatial, exploitation of hyperspectral imagery.
One common application of hyperspectral analysis, referred to as target detection, is the detection of materials of interest that are rare in the image. The objective of target detection is to determine which pixels in a given hyperspectral image are likely to contain known target materials.
In the conventional paradigm for target detection, each pixel in the given image is assumed to belong to either a target class or a background class, and the objective is to determine to which class each pixel belongs. Conventionally, the first step in making this determination is to compare the signature of each pixel to a library of reference signatures for one or more target materials. For each comparison, a statistical goodness-of-fit score is computed in order to quantify the spectral match between the given pixel and each target signature. The score is often normalized relative to a background class using a model of the background derived from the image. A decision threshold can be applied to the scores in order to determine pixels in the image that are considered more likely to belong to the target class than to the background class. The utility of target detection is determined by the ability to identify a single decision threshold that adequately separates the distribution of target scores from the distribution of background scores.
In practice, however, the target and background distributions often overlap considerably. After applying the decision threshold, targets that score lower than the threshold are referred to as missed targets and background pixels that score higher are referred to as false alarms. All pixels that score higher than the decision threshold, including both targets and false alarms, are referred to as alarmed pixels or cues. In this context, the effectiveness of a decision threshold is determined by the number of missed targets and false alarms relative to the total number of cues.